Building a Generative AI Evaluation Framework

Generative artificial intelligence (gen AI) is the fundamental force driving major advancements in multiple industries, such as manufacturing, retail, and healthcare. 

Due to its significant benefits in delivering value, gen AI's adoption is consistently increasing. The latest McKinsey survey reports that 65% of organizations globally regularly use gen AI.

However, implementing generative AI technology is challenging, requiring organizations to evaluate gen AI’s performance for specific use cases. Unlike traditional AI, where straightforward automated evaluation techniques help assess model performance, gen AI’s evaluation is more complex.

In this post, we will discuss the challenges associated with gen AI evaluation, factors to consider before evaluation, the steps to build an effective evaluation framework, and how you can use Encord to speed up your evaluation workflows.

Challenges of Evaluating Gen AI Models

Straightforward evaluation metrics, such as prediction accuracy, precision, and recall, are insufficient to assess generative AI models. This is because such models come with unique challenges that make their evaluation more complex than usual.

Here is a list that highlights a few of these issues.

• Subjectivity: When evaluating gen AI large language models (LLMs), subjectivity plays a significant role, as qualities like creativity or coherence are challenging to quantify and often require human judgment.
• Bias in datasets: Developing gen AI systems requires extensive training data with clear labels. However, detecting inherent biases in such large datasets is tricky. Biased data can lead to skewed outputs, propagating or even amplifying societal biases.
• Scalability: Robust model evaluation demands extensive resources, which can be hard to scale across diverse applications. This becomes even more challenging when implementing continuous monitoring frameworks to evaluate gen AI model performance in real-time.
• Interpretability: Interpreting or explaining gen AI’s internal process is complex, as understanding how and why it makes certain decisions is difficult. The exact decision-making mechanism remains a black box, making it difficult for experts to gain actionable insights for improvement.

Factors to Consider Before Building Evaluation Frameworks

Although the challenges above make gen AI evaluation difficult, experts can address them by building a comprehensive evaluation pipeline. The approach requires considering a few factors, as discussed below.

• Task Type: Different generative tasks, such as text generation, summarization, image synthesis, or code completion, have unique requirements and quality expectations. Experts must consider tailoring the evaluation strategy according to these specific needs. For example, experts can measure coherence in text, realism in images, or accuracy in code.
• Data Type: Experts must consider the data type used in their AI solutions to determine the evaluation approach. Generative AI applications usually use unstructured data such as text, images, and videos. Each data type demands unique metrics. For instance, text might require metrics that measure linguistic diversity, while images might use measures to assess image clarity and brightness.
• Computational Complexity: Evaluation can be resource-intensive, particularly for complex models. When setting up an evaluation framework, consider the computational cost to ensure it is feasible for ongoing assessments without excessive demands on resources or time.
• Need for Model Interpretability and Observability: With responsible AI becoming increasingly critical, understanding how a generative model produces outputs is essential. Such interpretability and observability allow experts to address potential biases, enabling more informed decision-making and accountability.

Learn more about model observability in machine learning

How to Build a Gen AI Evaluation Framework?

With the above factors in mind, experts can build a framework to evaluate Gen AI systems across the entire development lifecycle. 

Although the exact steps to implement such a framework may vary from case to case, the list below offers a starting point for building an evaluation framework.

Define the Problem and Objectives

The first step in building a gen AI evaluation framework is clearly defining the problem and objectives. This involves specifying the purpose of the generative model, the tasks it will perform, and the outcomes expected from its deployment.

Defining the problem and establishing these objectives will rely heavily on the use case for which you are building the generative model. For instance, is the model intended for content generation, producing realistic images for media, or creating code for software development pipelines? Each of these use cases comes with its own unique set of requirements and success criteria.

Once the task is clear, you must set concrete evaluation objectives that align with technical and user-focused goals. Here, you will need to answer the question of what you should measure to assess quality. Involvement from relevant stakeholders is essential to ensure alignment with company-wide performance standards.

Answering this will help shape the choice of data sources, evaluation metrics, and methods, ensuring they accurately reflect the model's intended role. This stage is crucial to developing a tailored, purposeful, and effective evaluation framework.

Defining Performance Benchmarks

After defining what to measure, you must identify relevant performance benchmarks to determine if the gen AI model meets its desired goals. Besides the task type, the choice of such benchmarks will depend on the type of gen AI model you develop.

Mainstream gen AI model categories include large language models (LLMs), retrieval-augmented generation (RAG) systems, and multimodal frameworks such as vision-language models (VLMs).

LLMs

Assessing LLM performance typically entails establishing benchmarks for hallucination, response relevance, and toxicity. Experts must determine how state-of-the-art (SOTA) LLMs, such as ChatGPT, perform to establish industry-accepted benchmarks.

This approach will also help identify the standard metrics and datasets developers use to measure such factors. For example, experts can use the Massive Multitask Language Understanding (MMLU) dataset to assess how well their LLM understands different subjects. 

It covers topics in STEM, social sciences, and humanities fields and tests world knowledge with problem-solving ability.

RAGs

RAG models augment LLM capabilities by combining information retrieval with text generation. This means developers must identify benchmarks that measure retrieval quality, response speed, and relevance to domain-specific user queries. They can use RAGBench as the benchmark dataset to measure RAG performance.

VLMs

Multimodal models, such as VLMs, require benchmarks that assess cross-modal understanding. This can mean computing similarity metrics between image, text, audio, and other modalities to determine alignment. 

For example, developers can measure image-captioning quality using a similarity score as the benchmark to evaluate a popular VLM called Contrastive Language-Image Pre-training (CLIP). They can compute the score by comparing the generated image captions with ground-truth labels.

CLIP Model

The higher the similarity between ground truth and predicted labels, the better the performance. COCO and ImageNet are popular benchmark datasets for such models.

Data Collection

Data collection is the next step in building a Gen AI evaluation framework. High-quality, representative data is essential for accurately assessing model performance. The data gathered should mirror the model’s real-world applications, capturing the diversity and complexity of inputs it will encounter.

For example, data should include varied conversational queries and tones when evaluating a language model for natural language tasks.

It is also essential to consider the reliability of data sources and ethical factors. Collected data should be free of biases that can skew the model’s outputs. This means attention to diversity in demographics, cultural perspectives, and subject matter is crucial.

Finally, collection methods must align with privacy and compliance standards, especially for sensitive data. By carefully gathering a high-quality, relevant dataset, you can ensure the evaluation framework can better capture how the model will perform in real-world scenarios.

Data Preprocessing

After collecting the relevant data, preprocessing is the next critical step in setting up an evaluation framework. It ensures data quality, consistency, and readiness for analysis. This process begins with data cleaning, removing irrelevant, noisy, or redundant information to create a more streamlined dataset that reflects the intended use case.

Data annotation is another essential aspect, where you label the data for specific attributes depending on the model’s task. For instance, in a language model for a question-answering task, annotations may include answers to questions that experts think users will typically ask. For VLMs, annotations might cover image-object relationships or alignment with descriptive text.

Annotators must carefully label specific data samples as the process can be highly subjective. For instance, ground-truth descriptions for particular images for an image-captioning VLM can vary from one annotator to another.

Consistent labeling across different samples requires regular reviews from domain experts and well-defined annotation standards to guide the labeling process.

Feature Engineering

Once data preprocessing is complete, the next stage is to extract relevant features from data that will be the primary inputs to your gen AI evaluation frameworks. This approach requires feature engineering—a process for identifying and transforming data characteristics to enhance assessment accuracy. The primary goal is to select and create features that reflect the qualities a generative model aims to optimize.

This differs from traditional feature engineering approaches for developing straightforward machine learning (ML) models. For instance, in conventional ML models like regression or decision trees, experts can extract straightforward, domain-specific features such as age, income, or transaction amount to predict outcomes.

In contrast, gen AI models require feature engineering that captures nuanced, often abstract qualities. For example, generating realistic images or coherent text involves features that reflect more subjective metrics like "creativity," "naturalness," or "semantic alignment," which are difficult to define and measure.

This difference in approach highlights the need for automation to create more sophisticated, context-aware features in gen AI evaluation.

Embeddings play a significant role in feature engineering for gen AI models. Experts can generate embeddings for unstructured data, such as text and images, using relevant AI algorithms. 

Embeddings

These embeddings represent the semantic properties of data samples through numerical vectors. Developers often use convolutional neural networks (CNNs) to generate image embeddings and Word2Vec to create text embeddings. 

CNNs using feature maps to create image embeddings

They measure the similarity between the image and textual embeddings to assess how well the generated images match textual descriptions in text-to-image models.

Selecting a Foundation Model

Since building a gen AI framework from scratch requires extensive computational power, a more pragmatic approach is using an open-source foundation model that aligns with your evaluation objectives. Such models are pre-trained on extensive datasets, giving them diverse knowledge on different subjects.

For instance, GPT-3 by OpenAI is a popular text generation foundation model. Similarly, CLIP and DALL-E are well-known VLMs for image captioning and generation tasks.

DALL-E Architecture

The choice of the foundation model directly impacts the evaluation strategy you use. Different models have varying strengths, architectures, and pre-trained knowledge, influencing the evaluation metrics and methods.

For example, DALL-E and Stable Diffusion are both text-to-image models. However, they differ in architecture and the style of images they create. You must choose the one that aligns with your objectives and evaluation benchmarks in the previous steps.

Fine-tuning

Once you have the foundation model, you can use its API as the building block for your own Gen AI model. For instance, you can create a chatbot that uses the GPT-3 API to generate text. However, relying solely on the foundation model may give poor evaluation results if your task is domain-specific.

This is because foundation models have generic knowledge, making them unsuitable for tasks requiring specialized information. For example, you must adapt the GPT-3 model to create a chatbot for medical professionals.

Fine-tuning

Fine-tuning is a key strategy for tailoring a foundation model to specific gen AI evaluation tasks. It takes a pre-trained model and adjusts its internal parameters with task-specific data. The method improves performance on specialized tasks like summarizing medical reports or answering questions regarding specific diseases.

RLHF for LLMs

Reinforcement learning with human feedback (RLHF) is a valuable fine-tuning approach that combines human feedback to train a foundation model. It includes humans giving scores to a gen AI model’s output and a reward model using these scores to adjust the generative model’s performance.

Evaluation

After model fine-tuning comes the evaluation stage. Here, you can measure model performance using the benchmark datasets and metrics selected in the second step. You can combine human and automated techniques for a more robust evaluation framework.

Automated techniques include computing metrics such as BLEU, ROUGE, or FID for natural language tasks. It can also involve computing similarity scores by comparing embeddings of the generated and ground-truth samples.

Meanwhile, human-based evaluation can be quantitative and qualitative. For instance, a quantitative method may have humans assigning scores to LLM responses. These scores can indicate how well the response relates to user queries.

On the other hand, qualitative assessments may focus on more detailed, subjective user feedback. Evaluators may provide narrative comments or detailed critiques, offering more profound insights into model behavior.

Continuous Monitoring

Continuous monitoring is the final step in the gen AI evaluation framework. It ensures that model performance remains consistent and aligned with its intended goals throughout its lifecycle.

Developers can create monitoring pipelines that regularly track outputs to detect issues like bias, drift in performance, or deviation from ethical benchmarks. 

Automated tools can flag anomalies, while periodic human evaluation can help assess subjective aspects like creativity or user satisfaction.

Encord Active for Evaluating AI Models

Encord Active is an AI-based evaluation platform for monitoring large-scale datasets for computer vision (CV) tasks. It supports active learning pipelines for evaluating data quality and model performance.

Encord Active

Key Features

• Scalability: Encord can help you scale evaluation pipelines by ingesting petabytes of data. You can create multiple datasets to manage larger projects and upload up to 200,000 frames per video at a time.
• Ease-of-Use: Encord offers an easy-to-use, no-code UI with self-explanatory menu options and powerful search functionality for quick data discovery.
• Integrations: Encord supports integration with mainstream cloud storage platforms such as AWS, Microsoft Azure, and Google Cloud. You can also programmatically control workflows using its Python SDK.

G2 Review

Encord has a rating of 4.8/5 based on 60 reviews. Users highlight the tool’s simplicity, intuitive interface, and several annotation options as its most significant benefits. 

However, they suggest a few areas for improvement, including more customization options for tool settings and faster model-assisted labeling for medical imagery.

Overall, Encord’s ease of setup and quick return on investments make it popular among data experts.

📌 Learn how to evaluate CLIP using Encord Active

Gen AI Evaluation Frameworks: Key Takeaways

As Gen AI’s applications evolve, a robust evaluation framework will define an organization’s ability to leverage the technology’s ability to drive productivity.

The list below highlights a few key points to remember regarding Gen AI evaluation frameworks.

• Gen AI Evaluation Challenges: Subjectivity, data bias, scalability, and interpretability are some of the most common challenges in evaluating gen AI frameworks.
• Steps to Build Gen AI Evaluation Framework: Businesses must first define clear goals, identify performance benchmarks, collect and process relevant data, extract data features, choose and fine-tune a foundation model, evaluate it, and continuously monitor it in production.
• Using Encord Active for Evaluation: Encord Active contains features to validate your entire CV development lifecycle from the ground up. It can help you test models and data through several metrics and interactive dashboards.